Build audience segments around needs you can observe and decisions you can change—not demographic labels used as a shortcut for motivation. Start by naming the decision, then test a hypothesis about people’s situations, behaviors, barriers, and desired outcomes. Demographic and contextual details still belong when they demonstrably affect access, eligibility, language, geography, or another measured decision.
Start with the decision your segments must improve
Before collecting data or naming groups, write down what a segment will change: a content pathway, product feature, message, service, or channel choice. If no decision would change, segmentation risks becoming a descriptive exercise rather than a useful tool.
For each project, check whether the decision is relevant, timely, feasible, and measurable, and compare what you learn with the wider audience. Those are practical criteria in Salesforce’s guide to market segmentation.
Form a hypothesis about a need, not an identity
Write a short, testable description of a situation and need you expect to find. For example: “People who need to complete a task quickly while away from a desktop may need a shorter mobile path.” This is a hypothesis, not a segment definition or a claim that everyone in a particular age or income group behaves alike.
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Google News Initiative recommends developing an audience hypothesis and testing it with user research. Its guiding question—“How do you confirm your assumptions about your target audiences and their needs?”—has a direct answer: ask people, then check what they do. See its audience understanding playbook.
Ask what people were trying to do and what got in the way
Interview about a recent, specific experience rather than asking people to predict their future behavior or choose an abstract identity label. Useful prompts include:
- What were you trying to do?
- What prompted you to do it at that time?
- What did you try first?
- What got in the way?
- What would a good outcome have looked like?
Turn the recurring pattern into a job story: “When [situation], I want to [need or action], so that [outcome].” Keep the situation, action, and desired result distinct: the task someone performs is not necessarily the underlying reason they perform it. Google’s playbook explains this distinction and suggests that personas may evolve after interviews or prove less useful than jobs-to-be-done.
Rank #2
- Ages 10+
- 40 minute playing time
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For a news or information service, NSW Government’s OneCX Program recommends asking “why the user came to the site today, what problem they are trying to solve, and what is the job to be done.” Its audience research guidance also cautions that the audience may not fit neatly into groupings.
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Combine what people say with what they do
Use multiple evidence sources because each answers a different question. Interviews and open-ended survey responses can reveal goals and barriers; analytics and feedback can show actions, content choices, and recurring points of friction. Salesforce lists analytics, surveys, interviews, reviews, and social interactions among possible inputs.
- Interviews and open responses: help explain context, interpretation, and motivation.
- Site or app behavior: shows actions in a particular product context, such as viewing a page or using a feature.
- Customer feedback: can surface unmet needs and problems people choose to report.
Google Ad Manager documents first-party segments based on how people use a site or app and the content they see. These signals establish observed behavior in context; by themselves, they do not prove a durable motive. The cited Audience Solutions feature is available only with Ad Manager 360, so it is not a capability every publisher can assume. Google’s documentation discusses using PPID rather than third-party cookies for greater coverage and durability; that recommendation does not remove the need for appropriate privacy governance. See Google Ad Manager’s audience solutions documentation.
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Keep groups separate only when they need different treatment
Compare candidate groups by the decision they serve. Ask whether people in each group need different information, product support, messages, or channels. If the answer is no, keep them in a broader segment rather than multiplying labels without an operational reason.
For product or content design, compare the outcome sought, constraints encountered, and help required. For campaign activation, also assess whether people can be reached through the intended channel and how reliably available signals assign them to the right group.
| Comparison question | Why it matters |
|---|---|
| Does the distinction change the decision? | A segment should lead to meaningfully different treatment. |
| How strong is the evidence? | Separate reported needs from observed actions and inferred motives. |
| Can membership be assigned clearly? | Vague rules make groups difficult to use consistently. |
| Who can be reached, and how? | A useful design segment may not be reachable for a particular campaign. |
| What is the risk of misclassification? | People assigned incorrectly may receive irrelevant or harmful treatment. |
| What data sensitivity and service cost are involved? | More detailed targeting can create privacy, governance, and operational burdens. |
Demographic, interest-based, and purchase-intent categories can be useful for organizing or comparing audience labels. The IAB Tech Lab’s Audience Taxonomy distinguishes these types and is intended to improve clarity across providers; version 1.1 followed a 30-day public comment period in October 2020. Taxonomy labels are a naming framework, not evidence that people sharing a label have the same need or that one segmentation method performs better. See the IAB Tech Lab Audience Taxonomy standards page.
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Define segment membership so another person can apply the rule
For each segment, record the qualifying evidence, time window, exclusions, data source, confidence, and owner. A usable rule should be clear enough that someone else could determine whether a case qualifies and explain why.
Google Ad Manager gives “Restaurant Enthusiasts” as a platform example defined by two page views or app activities in three days. That is an example of an operational behavior rule for that platform—not a universal threshold and not proof that those people are enthusiasts in a motivational sense.
Keep uncertainty visible. Note whether membership is based on what someone said, an observed action, or an inference; record important limits in the definition rather than presenting the segment as a fixed personal trait.
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Validate the segments, look for exceptions, and revise them
Google News Initiative suggests starting with 10 to 20 interviews to test assumptions, while emphasizing recruitment of relevant potential users and questions about lived experience. Treat that range as a practical initial suggestion, not a statistical representativeness standard or a guarantee that research has reached saturation.
- Recruit for the situation or need. Include people who have encountered the relevant task or constraint, rather than relying only on demographic proxies.
- Compare outcomes with a sensible baseline. Check whether the proposed groups differ in a way that matters to the decision, not just in a chart or profile.
- Seek contradictory cases. Look for people who meet the rule but do not share the expected need, and for people with the need who fail the rule.
- Update the definition. Change the segment when new evidence alters the situation, behavior, or outcome that makes the distinction useful.
Personas can help a team communicate a pattern, but a composite character should not be mistaken for a claim about every person in a segment. NSW Government says its first research callout produced over 70 personas and that its website audience did not fit neatly into groupings; it moved toward consolidating the work into mindsets and archetypes. The same program reported more than 14 million users on its new website over the first six months of the COVID-19 pandemic. Those figures describe that program and period, not a benchmark for other organizations.
Use demographic details when evidence shows they matter
The point is not to discard demographic or contextual data. Use it when it changes access, eligibility, language, geography, or a measured decision—for example, where a service is legally available only in certain places. The mistake is treating a shared demographic label as proof of shared motivation without checking whether the people involved actually face the same situation or want the same outcome.
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